Discretization of the Ergodic Functional Central Limit Theorem
Gilles Pag\`es, Cl\'ement Rey

TL;DR
This paper investigates how discretizing the ergodic functional CLT affects convergence rates, extending the theorem to numerical approximations and applications to Markov diffusion processes.
Contribution
It introduces a discretization method of order q for the ergodic CLT and establishes a corresponding q-order CLT, including applications to diffusion process approximations.
Findings
Discretization of order q preserves the ergodic CLT with a rate of n^{q/(2q+1)}.
Results apply to both stationary processes and q-weak order approximations.
Applications include Euler and Talay schemes for Brownian diffusions.
Abstract
In this paper, we study the discretization of the ergodic Functional Central Limit Theorem (CLT) established by Bhattacharya (see \cite{Bhattacharya_1982}) which states the following: Given a stationary and ergodic Markov process with unique invariant measure and infinitesimal generator , then, for every smooth enough function , converges in distribution towards the distribution of the process with a Wiener process. In particular, we consider the marginal distribution at fixed , and we show that when is replaced by a well chosen discretization of the time integral with order ( Riemann discretization in the case ), then the CLT still holds but with rate…
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Taxonomy
TopicsStochastic processes and financial applications · Insurance, Mortality, Demography, Risk Management · Financial Risk and Volatility Modeling
